探索因特网提供的失眠认知行为治疗中断:对患病率,自我报告的原因以及基线和干预数据作为预测因素的二次分析
Laura Simon1, Lisa Steinmetz2, Eileen Bendig1
1Institute of Psychology and Education, Department of Clinical Psychology and Psychotherapy, Ulm University, Ulm, Germany.
概括
在因特网提供的失眠认知行为疗法 (iCBT-I) 中,退学是一个重大挑战,影响超过40%的患者. 早期干预的使用,而不是基线因素,可以预测数字治疗中的学风险.
科学领域:
- 数字治疗学数字治疗学
- 行为科学是一种行为科学.
- 睡眠的药物 睡眠的药物
背景情况:
- 通过互联网提供的失眠认知行为疗法 (iCBT-I) 是一种有效的失眠治疗方法.
- 退学仍然是数字疗法的一个重大挑战,可能会限制治疗疗效.
- 了解退学原因和预测因素对于改善iCBT-I的用户保留至关重要.
研究的目的:
- 分析自己报告的退出iCBT-I的原因.
- 调查基线变量是否预测iCBT-I的退学情况.
- 确定早期干预使用数据是否可以预测iCBT-I.
主要方法:
- 临床试验的探索性二次分析在一个阶段性护理iCBT-I模型.
- 通过自主开发的调查项目评估学原因.
- 用于探索脱学基线和使用预测器的两变模型.
主要成果:
- 在233名患者中,超过44%的人退出了iCBT-I干预.
- 学的主要原因包括生活中的分心,感知到缺乏有用性,以及在休息后恢复困难.
- 无论是基线变量还是人口统计数据都没有预测学;然而,完成第一个模块的时间和第一个星期的睡眠日记条目都是重要的预测因素.
结论:
- 退学是iCBT-I中普遍存在的问题,需要制定提高用户参与度的策略.
- 感知到的有用性和管理日常生活的分心是影响中断的关键因素.
- 早期治疗参与度指标显示,预测脱学风险和告知保留策略具有前景.
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